Preventing Algorithmic Defamation: A Closer Look at Auto-Complete Misuse

Exploring the case of algorithmic defamation, where auto-complete features in search engines perpetuate harmful information. Learn how responsible AI governance can mitigate such incidents.

Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

MEASURE 2.10 — similarity 0.523, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.507, rank 2. TAIM detail and related incidents →
GOVERN 1.7 — similarity 0.498, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/45

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

Pre-print on arXiv · Database snapshots & citation guide

We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.